Anthropic
Previous flagship Claude Opus release retained for compatibility after later Claude Opus launches. Still strong for advanced reasoning, extended thinking, computer use, and coding, but superseded by Opus 4.8 for Anthropic's latest Opus-tier performance.
Previous flagship Claude Opus release retained for compatibility after later Claude Opus launches.
45.9
Quality Score
1297
Arena ELO
Undisclosed
Parameters
1M
Context
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Dec 2025
Released
Launches
3
Pricing
1
Benchmarks
17
Research
3
General
6
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
A conversation with Boris Cherny and Cat Wu on the path from Claude Code to Claude Tag, and how it spread from engineering to the rest of Anthropic. Claude Fable 5 is now available in Claude Tag. https://t.co/8oNM5WaWzj
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Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
SWE-Bench Verified resolved rate 75.6
Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Available now in beta. https://t.co/HKhLknxLJO
View sourceAnnouncing Built with Claude: Life Sciences, a global virtual hackathon. Join us and @GladstoneInst for a week of researching and building with Claude Science and Claude Code, with a prize pool of $100k in credits. https://t.co/wzrSBHJgeP
View sourceSWE-Bench Verified resolved rate 75.6
View sourceSquidsoup is a collective of artists and designers who make immersive experiences with sound, light and space. We caught up with them before one of their largest projects to date: a live performance with an orchestra at the Southbank Centre in London. https://t.co/8wvgOYfotp
A conversation with Boris Cherny and Cat Wu on the path from Claude Code to Claude Tag, and how it spread from engineering to the rest of Anthropic. Claude Fable 5 is now available in Claude Tag. https://t.co/8oNM5WaWzj
Announcing Built with Claude: Life Sciences, a global virtual hackathon. Join us and @GladstoneInst for a week of researching and building with Claude Science and Claude Code, with a prize pool of $100k in credits. https://t.co/wzrSBHJgeP
Claude Fable 5 will be available again globally tomorrow. After a series of productive conversations with the US government, we're redeploying the model with a new set of classifiers to target and block more cybersecurity tasks. In the near term, some routine tasks like coding
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on
Introducing Claude Science, a new app designed with every stage of research in mind. Artifacts traced to their code, environments managed on demand, and 60+ optional scientific databases that you can connect. Available now in beta. https://t.co/HKhLknxLJO
Since June 12, we’ve been working closely with the US government to restore access to Claude Mythos 5 and Fable 5. Today, the government notified us that Mythos 5, our strongest cybersecurity model, can be redeployed to a set of US organizations that operate and defend critical
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Quality: 92/100 | Speed: 40/100 | Price: $15/M tokens | MMLU: 91.5% | HumanEval: 92.5%
Large language models (LLMs) and agentic systems have shown promise for clinical decision support, but existing works largely assume that evidence has already been curated and handed to the model. Real-world clinical workflows instead require agents to actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources. In this paper, we introduce ClinSeekAgent, an automated agentic framework for dynamic multimodal evidence seeking that shifts the paradigm from passive evidence consumption to active evidence acquisition. Given only a clinical query and access to raw data sources, ClinSeekAgent gathers evidence by querying medical knowledge bases, navigating raw EHRs, and invoking medical imaging tools; refines its hypotheses as new information emerges; and integrates the collected evidence into grounded clinical decisions. ClinSeekAgent serves both as an inference-time agent for frontier LLMs and as a training-time pipeline for distilling high-quality agent trajectories into compact open-source models. To validate its inference-time effectiveness, we construct ClinSeek-Bench, which pairs Curated Input reasoning from fixed pre-selected evidence with Automated Evidence-Seeking over raw clinical data. On text-only EHR tasks, ClinSeekAgent improves Claude Opus 4.6 from 60.0 to 63.2 overall F1 and MiniMax M2.5 from 43.1 to 47.3, with positive risk-prediction gains in 7 out of 9 evaluated host models. On multimodal tasks, ClinSeekAgent improves Claude Opus 4.6 from 47.5 to 62.6 (+15.1); all evaluated models improve across the three CXR-related task groups. We further validate ClinSeekAgent as a training pipeline by distilling agentic evidence-seeking trajectories into ClinSeek-35B-A3B, which achieves 34.0 average F1 on existing AgentEHR-Bench, improving over its Qwen3.5-35B-A3B baseline by +11.9 points and approaching Claude Opus 4.6.
Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end-to-end omni-modal tool use in the real world. To address this gap, we introduce MM-ToolBench, a benchmark and evaluation harness for task-oriented omni-modal tool use. MM-ToolBench contains 100 executable tasks from two macro task families, Customer Service and Intelligent Creation, covering 20 subcategory slices and supported by 27 MCP servers with 324 tools. The central design of MM-ToolBench is closed-loop multimodal verification: agents must execute tools, inspect rendered or transformed artifacts, and self-correct when outputs fail task-specific requirements. To make such evaluation scalable and verifiable, MM-ToolBench couples MCP-based execution with task-specific grounded evaluators and a semi-automated construction pipeline for scenario discovery, task instantiation, evaluator synthesis, and human audit. Experiments on 15 contemporary agentic models show that MM-ToolBench remains highly challenging: Claude Opus 4.6, commonly regarded as one of the strongest coding-agent models, achieves only 32.0% task success, far below the 94.0% human benchmark. We envision MM-ToolBench as a practical foundation for evaluating and advancing next-generation omni-modal tool-using agents through closed-loop multimodal verification.
Monitoring complex industrial assets relies on engineer-authored symbolic rules that trigger based on sensor conditions and prompt technicians to perform corrective actions. The bottleneck is not detection but response: translating rules into maintenance steps requires asset-specific knowledge gained through years of practice. We investigate whether LLMs can serve as decision support for this rule-to-action step and introduce , a benchmark of 6{,}690 expert-validated multiple-choice questions from 118 rule-action pairs across 16 asset types. We contribute (i) a symbolic-to-MCQA pipeline normalizing rules to Disjunctive Normal Form with embedding-based distractor sampling, (ii) five variants probing distinct failure modes (Pro, Pert, Verbose, Aug, Rationale), and (iii) a benchmark of 29 LLMs and 4 embedding baselines. A human evaluation (9 practitioners, mean 45.0\%) confirms requires specialist knowledge beyond operational experience. Three findings stand out. The frontier has closed: the top three LLMs lie within one Macro point, with Bradley-Terry Elo placing claude-opus-4-6 30 points above the next model. Yet \,Pro exposes brittleness, with every model losing 13--60\% relative accuracy under distractor expansion. \,Aug exposes pattern-matching: under condition inversion, frontier models still select the original answer 49--63\% of the time. The deployment bottleneck is not capability but calibration: frontier models handle template-style fault detection but break under structural perturbation.
LiveCodeBench pass@1 62.4 across 1055 tasks
LiveCodeBench pass@1 62.4 across 1055 tasks